Metaheuristics are a class of algorithms that belong to the field of Computational Intelligence (a part of Artificial Intelligence). This is a family of algorithms that are inspired by nature that are capable of finding good solutions for “hard” optimization problems. Examples of these algorithms are Genetic Algorithms, Particle Swarm Algorithm, Ant Colony Algorithm, Simulated Annealing, etc…

This works present how to apply this type of algorithms to the problems of i) transducers desing optimization and ii) transducers materials selection optimization in the case of hard optimization problems.

The “hard” clasification can be produced by the presence of severe constrains (most likely due to materials availability) or by the dimension of the space of search or by the complexity of the taget function (multiple values, Pareto optimization, etc…), or by the fact that we have a mixed (continum and discrete) optimization problem.

Advantages and problems of these algorithms for this application are discussed and some examples are shown related to different multilayered transducers for thickness mode operation